library("car")
library("interplot")

`2015_c4g` <- read.csv("~/2015_c4g.csv")

model1 <- lm(mobilization ~ information*age+female+education+interest, data=`2015_c4g`)
model2 <- lm(mobilization ~ information*female+age+education+interest, data=`2015_c4g`)
model3 <- lm(mobilization ~ information*education+age+female+interest, data=`2015_c4g`)
model4 <- lm(mobilization ~ information*interest+age+female+education, data=`2015_c4g`)

interplot(m=model1, var1="information", var2="age") +
    xlab("Age") +
    ylab("Marginal effect of Information") +
    theme_bw() +
    geom_hline(yintercept = 0, linetype = "dashed")

interplot(m=model2, var1="information", var2="female") +
    xlab("Female") +
    ylab("Marginal effect of Information") +
    theme_bw() +
    geom_hline(yintercept = 0, linetype = "dashed")

interplot(m=model3, var1="information", var2="education") +
    xlab("Education") +
    ylab("Marginal effect of Information") +
    theme_bw() +
    geom_hline(yintercept = 0, linetype = "dashed")

interplot(m=model4, var1="information", var2="interest") +
    xlab("Political interest") +
    ylab("Marginal effect of Information") +
    theme_bw() +
    geom_hline(yintercept = 0, linetype = "dashed")

